A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.

While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional reg...

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Autores principales:	Jason Ernst, Qasim K Beg, Krin A Kay, Gábor Balázsi, Zoltán N Oltvai, Ziv Bar-Joseph
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6566cb809a4f4e4a8334003fa48614b4
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spelling	oai:doaj.org-article:6566cb809a4f4e4a8334003fa48614b42021-12-02T19:57:55ZA semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.1553-734X1553-735810.1371/journal.pcbi.1000044https://doaj.org/article/6566cb809a4f4e4a8334003fa48614b42008-03-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18369434/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer), a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic-anaerobic shift interface.Jason ErnstQasim K BegKrin A KayGábor BalázsiZoltán N OltvaiZiv Bar-JosephPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 3, p e1000044 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jason Ernst Qasim K Beg Krin A Kay Gábor Balázsi Zoltán N Oltvai Ziv Bar-Joseph A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.
description	While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer), a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic-anaerobic shift interface.
format	article
author	Jason Ernst Qasim K Beg Krin A Kay Gábor Balázsi Zoltán N Oltvai Ziv Bar-Joseph
author_facet	Jason Ernst Qasim K Beg Krin A Kay Gábor Balázsi Zoltán N Oltvai Ziv Bar-Joseph
author_sort	Jason Ernst
title	A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.
title_short	A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.
title_full	A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.
title_fullStr	A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.
title_full_unstemmed	A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.
title_sort	semi-supervised method for predicting transcription factor-gene interactions in escherichia coli.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/6566cb809a4f4e4a8334003fa48614b4
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A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.

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